
import pandas as pd
import geopandas as gpd
import contextily as ctx
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np


pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:,.0f}'.format


COVID_Total_rawdata = pd.read_csv('data/01_COVID_Total_Mover.csv')


COVID_Total_rawdata


COVID_Total_rawdata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Unnamed: 0    6 non-null      object
 1   2019          6 non-null      int64 
 2   2020          6 non-null      int64 
 3   Average_Case  6 non-null      int64 
dtypes: int64(3), object(1)
memory usage: 320.0+ bytes


COVID_Total_rawdata.columns = ['Month','2019','2020','Average_COVID_New_Case']


COVID_Total_rawdata


fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Scatter(y=COVID_Total_rawdata['2019'],
                         x=COVID_Total_rawdata['Month'],
                         text= COVID_Total_rawdata['2019'],
                         name="2019",
                         textposition = 'top center',
                         mode='lines+markers+text',
                         marker=dict(color='#5D69B1', size=8),
                         line=dict(color='#1a8dff', width=2),
                         texttemplate = "%{y:.3s}"
                        ),
             secondary_y=True
             )

fig.add_trace(go.Scatter(y=COVID_Total_rawdata['2020'],
                         x=COVID_Total_rawdata['Month'],
                         name="2020",
                         text= COVID_Total_rawdata['2020'],
                         textposition = 'top center',
                         mode='lines+markers+text',
                         marker=dict(color='#5D69B1', size=8),
                         line=dict(color='#ff1a1a', width=2),
                         texttemplate = "%{y:.3s}"
                        ),
              secondary_y=True
             )

fig.add_trace(go.Bar(y=COVID_Total_rawdata['Average_COVID_New_Case'], 
                     x=COVID_Total_rawdata['Month'],
                    text= COVID_Total_rawdata['Average_COVID_New_Case'],
                    name="Average Daily New COVID Cases",
                    width=[0.25,0.25,0.25,0.25,0.25,0.25]
                    ),
              secondary_y=False
             )

fig.update_layout(height=600, width=1000)
fig.update_layout(legend_title_text = "Year")
fig.update_xaxes(title_text="<b>Month in 2019 and 2020</b>")
fig.update_yaxes(title_text="<b>Average Daily New COVID Cases</b>", secondary_y=False)
fig.update_yaxes(title_text="<b>Monthly Moving</b>", secondary_y=True)

fig.show()


COVID_StateMoving_rawdata = pd.read_csv('data/02_COVID_State_Moving.csv')


COVID_StateMoving_rawdata


fig = go.Figure()

fig = px.bar(COVID_StateMoving_rawdata,
             y=COVID_StateMoving_rawdata['State'],
             x = COVID_StateMoving_rawdata['Net_Moving'],
             title='Top 5 States for Net Gain and Top 5 States for Net Loss of Moving',
             orientation = 'h',
             color=COVID_StateMoving_rawdata['Net_Moving'],
             color_continuous_scale='Viridis',
            )

fig.update_traces( textposition='outside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(height=600, width=1000)
fig.update_xaxes(title_text="<b>Net Gain or Loss</b>")
fig.update_yaxes(title_text="<b>State</b>")


fig.show()


COVID_AreaMoving_rawdata = pd.read_csv('data/03_COVID_Area_Moving.csv')


COVID_AreaMoving_rawdata


fig = go.Figure()

fig = px.bar(COVID_StateMoving_rawdata,
             y=COVID_AreaMoving_rawdata['Area'],
             x = COVID_AreaMoving_rawdata['Net_Moving'],
             title='Top 5 Cities/Areas for Net Gain and Top 5 Cities/Areas for Net Loss of Moving',
             orientation = 'h',
             color=COVID_AreaMoving_rawdata['Net_Moving'],
             color_continuous_scale='Viridis',
            )

fig.update_traces( textposition='outside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(height=600, width=1000)
fig.update_xaxes(title_text="<b>Net Gain or Loss</b>")
fig.update_yaxes(title_text="<b>Cities/Areas</b>")

fig.show()


metro_geodata_rawdata = gpd.read_file('data/05_Basemap_metroborder/tl_2019_us_cbsa.shp')


metro_geodata_rawdata.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 938 entries, 0 to 937
Data columns (total 13 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   CSAFP     550 non-null    object  
 1   CBSAFP    938 non-null    object  
 2   GEOID     938 non-null    object  
 3   NAME      938 non-null    object  
 4   NAMELSAD  938 non-null    object  
 5   LSAD      938 non-null    object  
 6   MEMI      938 non-null    object  
 7   MTFCC     938 non-null    object  
 8   ALAND     938 non-null    int64   
 9   AWATER    938 non-null    int64   
 10  INTPTLAT  938 non-null    object  
 11  INTPTLON  938 non-null    object  
 12  geometry  938 non-null    geometry
dtypes: geometry(1), int64(2), object(10)
memory usage: 95.4+ KB


columns_to_keep2=['GEOID','NAME','NAMELSAD','geometry']

metro_geodata_ready = metro_geodata_rawdata[columns_to_keep2]

metro_geodata_ready['State'] = metro_geodata_ready['NAME'].str[-2:]


metro_geodata_ready = metro_geodata_ready.drop(['NAME'],axis=1)

metro_geodata_ready.columns=['GEOID','Metro_Name','geometry','State']

/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:853: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


metro_geodata_ready.head()


metro_geodata_ready_contiguous = metro_geodata_ready
metro_geodata_ready_contiguous['Region'] = 'The_contiguous_US'

metro_geodata_ready_contiguous.loc[metro_geodata_ready_contiguous['State'] == 'PR','Region'] = 'Non_the_contiguous_US'
metro_geodata_ready_contiguous.loc[metro_geodata_ready_contiguous['State'] == 'HI','Region'] = 'Non_the_contiguous_US'
metro_geodata_ready_contiguous.loc[metro_geodata_ready_contiguous['State'] == 'AK','Region'] = 'Non_the_contiguous_US'


metro_geodata_ready_contiguous[metro_geodata_ready_contiguous.Region == 'The_contiguous_US'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f1ee1335280>


metro_geodata_ready_Contiguous = metro_geodata_ready[metro_geodata_ready.Region =='The_contiguous_US']


migration2018_rawdata = pd.read_csv('data/06_Migration_Metro_to_Metro_2018.csv',
                                    dtype={
                                       'Current Residence Metro Code1':str,
                                       'Residence 1 Year Ago Metro Code1':str
                                           }
                                           )

migration2018_rawdata.head()


migration2018_rawdata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 49739 entries, 0 to 49738
Data columns (total 16 columns):
 #   Column                                                 Non-Null Count  Dtype 
---  ------                                                 --------------  ----- 
 0   Current Residence Metro Code1                          49739 non-null  object
 1   Residence 1 Year Ago Metro Code1                       49739 non-null  object
 2   Metropolitan Statistical Area of Current Residence     49739 non-null  object
 3   Population 1 Year and Over                             49739 non-null  int64 
 4   Nonmovers                                              49739 non-null  int64 
 5   Movers within Same Metropolitan Statistical Area       49739 non-null  int64 
 6   Movers from Different Metropolitan Statistical Area2   49739 non-null  int64 
 7   Movers from Elsewhere in the U.S. or Puerto Rico       49739 non-null  int64 
 8   Movers from Abroad3                                    49739 non-null  int64 
 9   Metropolitan Statistical Area of Residence 1 Year Ago  49739 non-null  object
 10  Population 1 Year and Over.1                           49739 non-null  int64 
 11  Nonmovers.1                                            49739 non-null  int64 
 12  Movers within Same Metropolitan Statistical Area.1     49739 non-null  int64 
 13  Movers to Different Metropolitan Statistical Area2     49739 non-null  int64 
 14  Movers to Elsewhere in the U.S. or Puerto Rico         49739 non-null  int64 
 15  Movers in Metro-to-Metro Flow                          49739 non-null  int64 
dtypes: int64(12), object(4)
memory usage: 6.1+ MB


Columns_to_keep1 = ['Residence 1 Year Ago Metro Code1','Movers in Metro-to-Metro Flow']

by_OutMigration1 = migration2018_rawdata [Columns_to_keep1]


by_OutMigration1.head()


by_OutMigration1.columns =['GEOID','Out_Migration_Population']

by_OutMigration1.head()


by_OutMigration1_group = by_OutMigration1.groupby(by=["GEOID"]).sum()


by_OutMigration1_group.head()


by_OutMigration1_sort = by_OutMigration1_group.sort_values(by='Out_Migration_Population', ascending=False)


by_OutMigration1_sort.info()

<class 'pandas.core.frame.DataFrame'>
Index: 390 entries, 35620 to 30300
Data columns (total 1 columns):
 #   Column                    Non-Null Count  Dtype
---  ------                    --------------  -----
 0   Out_Migration_Population  390 non-null    int64
dtypes: int64(1)
memory usage: 6.1+ KB


by_OutMigration1_ready = metro_geodata_ready.merge(by_OutMigration1_sort,
                                                   on='GEOID'
                                                  )


by_OutMigration1_ready['Region'] = 'The_contiguous_US'

by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '39380','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '46520','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '11260','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '21820','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '45940','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '41980','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '38660','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '27980','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '10380','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '32420','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '11640','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '41900','Region'] = 'Non_the_contiguous_US'
by_OutMigration1_ready.loc[by_OutMigration1_ready['GEOID'] == '25020','Region'] = 'Non_the_contiguous_US'


by_OutMigration1_ready.head()


by_OutMigration1_ready_Contiguous = by_OutMigration1_ready[by_OutMigration1_ready.Region =='The_contiguous_US']


by_OutMigration1_ready_Contiguous.head()


migration1_web_mercator = by_OutMigration1_ready_Contiguous.to_crs(epsg=3857)
metromap_web_mercator = metro_geodata_ready_Contiguous.to_crs(epsg=3857)


fig, axs = plt.subplots(1,1, figsize=(50, 30))
ax = axs




migration1_web_mercator.plot(ax=ax,
                             cmap='OrRd',
                             column = 'Out_Migration_Population',
                             scheme='Quantiles',
                             legend=True,
                                                        )


metromap_web_mercator.plot(ax=ax,
                                                                           facecolor='none',
                                                                           edgecolor='white',
                                                                            lw=0.5,
                                                                            alpha=0.8)
ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, alpha=0.5)

ax.axis("off")
ax.set_title("Metro-Level Out-Migration Population",fontsize = 60)

Text(0.5, 1.0, 'Metro-Level Out-Migration Population')


by_OutMigration1_top5andbottom5 = by_OutMigration1_sort.iloc[np.r_[0:5, -5:0]]


by_OutMigration1_top5andbottom5 = by_OutMigration1_top5andbottom5.merge(metro_geodata_ready_contiguous,
                                                                        on='GEOID',
                                                                        how='left'
                                                                       )


by_OutMigration1_top5andbottom5


fig = px.bar(by_OutMigration1_top5andbottom5,
       y='Metro_Name',
       x = ['Out_Migration_Population'],
       title='Top 5 and Bottom 5 of Metro Regions with Out-Migration Population',
             orientation = 'h',
             )

fig.update_traces( textposition='outside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()


migration2018_rawdata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 49739 entries, 0 to 49738
Data columns (total 16 columns):
 #   Column                                                 Non-Null Count  Dtype 
---  ------                                                 --------------  ----- 
 0   Current Residence Metro Code1                          49739 non-null  object
 1   Residence 1 Year Ago Metro Code1                       49739 non-null  object
 2   Metropolitan Statistical Area of Current Residence     49739 non-null  object
 3   Population 1 Year and Over                             49739 non-null  int64 
 4   Nonmovers                                              49739 non-null  int64 
 5   Movers within Same Metropolitan Statistical Area       49739 non-null  int64 
 6   Movers from Different Metropolitan Statistical Area2   49739 non-null  int64 
 7   Movers from Elsewhere in the U.S. or Puerto Rico       49739 non-null  int64 
 8   Movers from Abroad3                                    49739 non-null  int64 
 9   Metropolitan Statistical Area of Residence 1 Year Ago  49739 non-null  object
 10  Population 1 Year and Over.1                           49739 non-null  int64 
 11  Nonmovers.1                                            49739 non-null  int64 
 12  Movers within Same Metropolitan Statistical Area.1     49739 non-null  int64 
 13  Movers to Different Metropolitan Statistical Area2     49739 non-null  int64 
 14  Movers to Elsewhere in the U.S. or Puerto Rico         49739 non-null  int64 
 15  Movers in Metro-to-Metro Flow                          49739 non-null  int64 
dtypes: int64(12), object(4)
memory usage: 6.1+ MB


Columns_to_keep5 =['Residence 1 Year Ago Metro Code1',
                   'Metropolitan Statistical Area of Residence 1 Year Ago',
                  'Population 1 Year and Over.1',
                  'Nonmovers.1',
                  'Movers within Same Metropolitan Statistical Area.1',
                  'Movers to Different Metropolitan Statistical Area2',
                  'Movers to Elsewhere in the U.S. or Puerto Rico']

migration2018_OutMigration_Rawdata = migration2018_rawdata[Columns_to_keep5]


migration2018_OutMigration_Rawdata.columns = ['GEOID','Metro_Name','Population','Non_Movers','Within_Metro','Different_Metro','Non_Metro']


migration2018_OutMigration_Rawdata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 49739 entries, 0 to 49738
Data columns (total 7 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   GEOID            49739 non-null  object
 1   Metro_Name       49739 non-null  object
 2   Population       49739 non-null  int64 
 3   Non_Movers       49739 non-null  int64 
 4   Within_Metro     49739 non-null  int64 
 5   Different_Metro  49739 non-null  int64 
 6   Non_Metro        49739 non-null  int64 
dtypes: int64(5), object(2)
memory usage: 2.7+ MB


migration2018_OutMigration_Rawdata = migration2018_OutMigration_Rawdata.drop_duplicates()


migration2018_OutMigration_Rawdata['Out_Migration_Population'] = migration2018_OutMigration_Rawdata['Population'] - migration2018_OutMigration_Rawdata['Non_Movers']


migration2018_OutMigration_Rawdata.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 390 entries, 0 to 4061
Data columns (total 8 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   GEOID                     390 non-null    object
 1   Metro_Name                390 non-null    object
 2   Population                390 non-null    int64 
 3   Non_Movers                390 non-null    int64 
 4   Within_Metro              390 non-null    int64 
 5   Different_Metro           390 non-null    int64 
 6   Non_Metro                 390 non-null    int64 
 7   Out_Migration_Population  390 non-null    int64 
dtypes: int64(6), object(2)
memory usage: 27.4+ KB


Top5_OutMigration_raw = migration2018_OutMigration_Rawdata.sort_values(by='Out_Migration_Population', ascending=False)
Top5_OutMigration = Top5_OutMigration_raw.head(5)
Top5_OutMigration


Top5_WithinMetro_raw = migration2018_OutMigration_Rawdata.sort_values(by='Within_Metro', ascending=False)
Top5_WithinMetro = Top5_WithinMetro_raw.head(5)
Top5_WithinMetro


Top5_DifferentMetro_raw = migration2018_OutMigration_Rawdata.sort_values(by='Different_Metro', ascending=False)
Top5_DifferentMetro = Top5_DifferentMetro_raw.head(5)
Top5_DifferentMetro


Top5_NonMetro_raw = migration2018_OutMigration_Rawdata.sort_values(by='Non_Metro', ascending=False)
Top5_NonMetro = Top5_NonMetro_raw.head(5)
Top5_NonMetro


fig = make_subplots(rows=1, 
                    cols=1,
                    shared_xaxes=True,
                    vertical_spacing=0.02
                    )

fig.add_trace(go.Bar(y=Top5_OutMigration['Metro_Name'], 
                     x=Top5_OutMigration['Out_Migration_Population'],
                    text= Top5_OutMigration['Out_Migration_Population'],
                     textposition="outside",
                     orientation = 'h',
                    name="Total Out-Migration_Population",
                    ),
              row=1, col=1)

fig.update_layout(height=500, width=1000,
                  title_text="Top 5 Metro Area for Out-Migration")

fig.update_layout(legend_title_text = "Metro Area")
fig.update_layout(legend_font_size=11)
fig.update_layout(legend_title_font_size=15)
fig.show()


fig = make_subplots(rows=3, 
                    cols=1,
                    shared_xaxes=True,
                    vertical_spacing=0.02
                    )


fig.add_trace(go.Bar(y=Top5_DifferentMetro['Metro_Name'], 
                     x=Top5_DifferentMetro['Different_Metro'],
                    text= Top5_DifferentMetro['Different_Metro'],
                     textposition="outside",
                     orientation = 'h',
                    name="Move to Different Metro Area"),
              row=1, col=1,)

fig.add_trace(go.Bar(y=Top5_WithinMetro['Metro_Name'], 
                     x=Top5_WithinMetro['Within_Metro'],
                    text= Top5_WithinMetro['Within_Metro'],
                     textposition="outside",
                     orientation = 'h',
                    name="Move within Metro Area"),
              row=2, col=1,)

fig.add_trace(go.Bar(y=Top5_NonMetro['Metro_Name'], 
                     x=Top5_NonMetro['Non_Metro'],
                    text= Top5_NonMetro['Non_Metro'],
                     textposition="outside",
                     orientation = 'h',
                    name="Move to Non-Metro Area",
                    ),row=3, col=1,)

fig.update_layout(height=1000, width=1000,
                  title_text="Top 5 Metro Area for Out-Migration")

fig.update_layout(legend_title_text = "Metro Area")
fig.update_layout(legend_font_size=11)
fig.update_layout(legend_title_font_size=15)
fig.show()

	Unnamed: 0	2019	2020	Average_Case
0	Feb	2030000	2090000	2
1	Mar	2400000	2910000	6077
2	Apr	2510000	2750000	29577
3	May	2740000	2630000	23340
4	June	2830000	2640000	28467
5	July	2870000	2960000	61882

	Month	2019	2020	Average_COVID_New_Case
0	Feb	2030000	2090000	2
1	Mar	2400000	2910000	6077
2	Apr	2510000	2750000	29577
3	May	2740000	2630000	23340
4	June	2830000	2640000	28467
5	July	2870000	2960000	61882

	State	Net_Moving
0	Michigan	45408
1	North Carolina	37971
2	Texas	30603
3	New Jersy	25738
4	Minnesota	25315
5	District of Columbia	-15638
6	Arizona	-41923
7	California	-87750
8	New York	-117251
9	Florida	-235765

	Area	Net_Moving
0	Katy, TX	4414
1	Richmond, TX	3000
2	Frisco, TX	2604
3	East Hampton, NY	2476
4	Georgetown, TX	2337
5	Leander, TX	2294
6	Cypress, TX	2147
7	Cumming, GA	2128
8	Riverview, FL	2093
9	Meridian, ID	2088
10	Fort Myers, FL	-11889
11	Philadelphia, PA	-12833
12	Houston, TX	-14883
13	Washington, DC	-15520
14	Naples, FL	-22100
15	Los Angeles, CA	-26438
16	San Francisco, CA	-27187
17	Chicago, IL	-31347
18	Brooklyn, NY	-43006
19	New York, NY	-110978

	GEOID	Metro_Name	geometry	State
0	12020	Athens-Clarke County, GA Metro Area	POLYGON ((-83.53739 33.96591, -83.53184 33.968...	GA
1	12060	Atlanta-Sandy Springs-Alpharetta, GA Metro Area	POLYGON ((-85.33823 33.65312, -85.33842 33.654...	GA
2	12100	Atlantic City-Hammonton, NJ Metro Area	POLYGON ((-74.85675 39.42076, -74.85670 39.420...	NJ
3	12120	Atmore, AL Micro Area	POLYGON ((-87.61542 31.04100, -87.61541 31.041...	AL
4	12140	Auburn, IN Micro Area	POLYGON ((-85.19295 41.38001, -85.19296 41.381...	IN

Notebook A1

`Research questions in this specific notebook:`

`Section 0. Import All Modules and Set Up Notebook`

`Section 1. Reproduce and Visualize Moving Trend During COVID`

`Section 1.1 Moving Trend in 2019 and 2020 COVID Time`

`Finding1: Moving trend during COVID and same period in 2019:`

`Section 1.2 Moving Trend in Various States`

`The Finding2: 5 states with most net gains and 5 states with most net loss of moving during COVID:`

`Section 1.3 Moving Trend in Various Cities and Areas`

`Finding3: Top 5 cities/areas with most net gains and Top 5 cities/areas with most net loss of moving during COVID:`

`Section 2. Capture National Out-Migration Trend in 2018`

`Section 2.1 Prepare Geo-Data for Metro Areas in the U.S.`

2. Migration CSV Data Process¶

3. Sort by Out-Migration Population¶

	Current Residence Metro Code1	Residence 1 Year Ago Metro Code1	Metropolitan Statistical Area of Current Residence	Population 1 Year and Over	Nonmovers	Movers within Same Metropolitan Statistical Area	Movers from Different Metropolitan Statistical Area2	Movers from Elsewhere in the U.S. or Puerto Rico	Movers from Abroad3	Metropolitan Statistical Area of Residence 1 Year Ago	Population 1 Year and Over.1	Nonmovers.1	Movers within Same Metropolitan Statistical Area.1	Movers to Different Metropolitan Statistical Area2	Movers to Elsewhere in the U.S. or Puerto Rico	Movers in Metro-to-Metro Flow
0	10180	10740	Abilene, TX Metro Area	167649	130384	21221	11121	3883	1040	Albuquerque, NM Metro Area	897771	773804	88078	28944	6945	3
1	10180	11100	Abilene, TX Metro Area	167649	130384	21221	11121	3883	1040	Amarillo, TX Metro Area	262324	212065	34679	11114	4466	234
2	10180	12060	Abilene, TX Metro Area	167649	130384	21221	11121	3883	1040	Atlanta-Sandy Springs-Roswell, GA Metro Area	5667591	4846011	625274	161709	34597	117
3	10180	12420	Abilene, TX Metro Area	167649	130384	21221	11121	3883	1040	Austin-Round Rock, TX Metro Area	1990877	1655990	241369	81180	12338	348
4	10180	12580	Abilene, TX Metro Area	167649	130384	21221	11121	3883	1040	Baltimore-Columbia-Towson, MD Metro Area	2757814	2390738	263600	97429	6047	8

	GEOID	Out_Migration_Population	Metro_Name	geometry	State	Region
0	35620	460501	New York-Newark-Jersey City, NY-NJ-PA Metro Area	POLYGON ((-74.88982 40.78773, -74.88971 40.787...	PA	The_contiguous_US
1	31080	365670	Los Angeles-Long Beach-Anaheim, CA Metro Area	MULTIPOLYGON (((-118.12590 33.69715, -118.1557...	CA	The_contiguous_US
2	16980	249120	Chicago-Naperville-Elgin, IL-IN-WI Metro Area	POLYGON ((-88.60224 41.63139, -88.61185 41.631...	WI	The_contiguous_US
3	47900	241889	Washington-Arlington-Alexandria, DC-VA-MD-WV M...	POLYGON ((-78.28480 38.75932, -78.29007 38.766...	WV	The_contiguous_US
4	41860	184898	San Francisco-Oakland-Berkeley, CA Metro Area	MULTIPOLYGON (((-122.61228 37.81522, -122.6260...	CA	The_contiguous_US
5	20220	2577	Dubuque, IA Metro Area	POLYGON ((-91.13089 42.47126, -91.13115 42.471...	IA	The_contiguous_US
6	19180	2442	Danville, IL Metro Area	POLYGON ((-87.92944 40.26969, -87.92943 40.270...	IL	The_contiguous_US
7	16220	2263	Casper, WY Metro Area	POLYGON ((-106.91286 42.43319, -106.91416 42.4...	WY	The_contiguous_US
8	37620	2047	Parkersburg-Vienna, WV Metro Area	POLYGON ((-81.58145 39.02618, -81.59519 39.031...	WV	The_contiguous_US
9	30300	1970	Lewiston, ID-WA Metro Area	POLYGON ((-116.91607 45.99536, -116.91626 45.9...	WA	The_contiguous_US

	GEOID	Metro_Name	Population	Non_Movers	Within_Metro	Different_Metro	Non_Metro	Out_Migration_Population
58	35620	New York-Newark-Jersey City, NY-NJ-PA Metro Area	19847550	17909377	1444576	460501	33096	1938173
48	31080	Los Angeles-Long Beach-Anaheim, CA Metro Area	13137459	11627233	1126203	365670	18353	1510226
14	16980	Chicago-Naperville-Elgin, IL-IN-WI Metro Area	9479646	8236723	953743	249120	40060	1242923
21	19100	Dallas-Fort Worth-Arlington, TX Metro Area	7073136	6006575	848993	174694	42874	1066561
35	26420	Houston-The Woodlands-Sugar Land, TX Metro Area	6593374	5650836	758679	148881	34978	942538

	Out_Migration_Population
GEOID
10180	8270
10380	11704
10420	25964
10500	5509
10540	6686